A 3D image dataset is a three-dimensional field of volumetric pixels (voxels) each of which has been assigned a gray or color value.
To generate a 3D image dataset, a sequence of individual 2D images is usually recorded. The 3D image dataset is generated from the individual 2D images with the aid of what is termed a reconstruction process; with the aid of, for instance, filtered back-projection.
A particular feature associated with cardiac imaging is that the heart is in constant motion. The time usually required to record a sequence of X-ray images is longer than the heartbeat period. The 2D X-ray images can not, therefore, be readily reconstructed from a recorded sequence into a 3D X-ray image dataset to practical effect.
For that reason, the approach has instead been adopted of performing an electrocardiogram (ECG) measurement on the patient during image recording and then measuring the heartbeat phase for each recording. What is measured as the heartbeat phase is the time elapsing between the occurrence in the electrocardiogram of a reference structure (usually what is termed an R peak) and the image recording instant (or vice versa).
The knowledge about the heartbeat phase can be applied in two different ways: A first sequence of X-ray images for each of which the heartbeat phase is measured can first be recorded with the aid of an X-ray C-arm. The sequence of X-ray images corresponds to different traversing angles of the X-ray C-arm. A range of 180 degrees plus what is termed the fan angle of the X-ray source is usually traversed. The time taken overall to record the X-ray image sequences is always the same. Whereas the heartbeat phase is to be randomly selected for the first sequence of X-ray images, the knowledge about the heartbeat phase can then be used for initiating the recording of further sequences of X-ray images in a defined manner. In other words, a succeeding traversal will in each case be triggered. That can be arranged such that an interval of heartbeat phases can after a predetermined number of traversals be defined in such a way that at each angular position of the X-ray C-arm there will be precisely one (or, in a progression thereof, at least one) X-ray image that has been assigned a heartbeat phase from the interval. The greater the number of traversals, the shorter can be the interval. In the case of N traversals, the interval will usually extend across an N-th of the entire range of heartbeat phases (from reference peak to reference peak). Since for each angular position there will be a complete set of X-ray images where the heartbeat phase occurs within the interval, a 3D X-ray image dataset can be reconstructed exclusively on the basis of said X-ray images. If the interval is sufficiently small, the structures will be sufficiently well defined. If, for instance, four traversals of the X-ray C-arm are selected, then a time resolution of one fourth of the heart's beat length will be achieved, which will suffice to see the ventricles and the large branches of the coronary arteries in the reconstruction images. The greater the number of traversals, the shorter will be the interval and the more there will be to see in the reconstruction images. The method just described is explained in more detail in the article by G. Lauritsch, J. Boese, L. Wigström, H. Kemeth, and R. Fahrig, titled “Towards Cardiac C-arm Computed Tomography”, appearing on pages 922 to 934 in IEEE Transactions on Medical Imaging, Vol. 25, published in 2006.
A second approach to employing the measured heartbeat phase to practical effect is described in DE 10 2004 048 209 B3.
X-ray images for each of which the heartbeat phase (cyclic relative time) has been measured are in that method grouped as a function of the measured heartbeat phase. A preliminary 3D image dataset is generated from each group. One of the 3D image datasets is selected as the reference image dataset. A movement matrix is then calculated from each of the other 3D image datasets in relation to the reference image dataset. What is referred to as a movement matrix (below, also “movement field”) is a three-dimensional vector field by means of which voxels or groups thereof in one 3D image dataset are linked to voxels or groups thereof in the other 3D image dataset. The movement matrix therein characterizes a “movement”, which is to say a spatial change in mutually corresponding image structures between the first 3D image dataset serving as the starting point and the second 3D image dataset. A correlation method, for example what is termed block matching known per se, or a method based on optical flow, is preferably used for calculating the movement matrix from the two 3D image datasets.
The movement matrix is in the method described in DE 10 2004 048 209 B3 subsequently employed for deforming the preliminary 3D image datasets. In other words, the preliminary 3D image datasets that have been assigned to any heartbeat phases are imaged onto the reference image dataset's situation. With the aid of the movement field, back-calculating is as it were performed from the 3D image dataset so that the situation prevailing during the heartbeat phase for which the reference image dataset was defined will be imaged. The defined preliminary 3D image datasets are then added together and a final 3D image dataset is obtained.